High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

DELS-MVS		98.19 53	98.77 59	97.52 53	98.29 62	99.71 16	99.12 41	94.58 63	98.80 110	95.38 53	96.24 120	98.24 74	97.92 108	99.06 40	99.52 1	99.82 16	99.79 45
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DeepC-MVS	97.63 4	98.33 49	98.57 62	98.04 41	98.62 57	99.65 23	99.45 26	98.15 23	99.51 17	92.80 103	95.74 134	96.44 92	99.46 21	99.37 19	99.50 2	99.78 34	99.81 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	96.92 7	98.67 38	99.05 45	98.23 37	99.57 26	99.45 68	99.11 42	94.66 58	99.69 4	96.80 32	96.55 115	99.61 53	99.40 25	98.87 58	99.49 3	99.85 10	99.66 113

MSLP-MVS++		99.15 18	99.24 35	99.04 15	99.52 32	99.49 63	99.09 44	98.07 29	99.37 27	98.47 8	97.79 83	99.89 35	99.50 16	98.93 50	99.45 4	99.61 129	99.76 64

IS_MVSNet		97.86 59	98.86 55	96.68 78	96.02 105	99.72 13	98.35 80	93.37 90	98.75 121	94.01 79	96.88 104	98.40 71	98.48 91	99.09 37	99.42 5	99.83 15	99.80 37

Vis-MVSNet (Re-imp)		97.40 75	98.89 54	95.66 111	95.99 108	99.62 33	97.82 103	93.22 96	98.82 107	91.40 119	96.94 101	98.56 69	95.70 166	99.14 35	99.41 6	99.79 31	99.75 72

PHI-MVS		99.08 22	99.43 20	98.67 28	99.15 45	99.59 45	99.11 42	97.35 39	99.14 65	97.30 27	99.44 13	99.96 12	99.32 32	98.89 55	99.39 7	99.79 31	99.58 129

APD-MVS		99.25 12	99.38 23	99.09 11	99.69 7	99.58 48	99.56 18	98.32 8	98.85 100	97.87 19	98.91 43	99.92 28	99.30 35	99.45 15	99.38 8	99.79 31	99.58 129
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	97.74 3	98.34 48	99.46 13	97.04 66	98.82 52	99.33 95	96.28 153	97.47 38	99.58 9	94.70 65	98.99 37	99.85 40	97.24 127	99.55 10	99.34 9	97.73 211	99.56 135

MVS_0304		98.81 33	99.44 17	98.08 39	98.83 51	99.75 9	99.58 17	95.53 46	99.76 1	96.48 38	99.70 4	98.64 66	98.21 96	99.00 46	99.33 10	99.82 16	99.90 7

DeepC-MVS_fast	98.34 1	99.17 17	99.45 14	98.85 24	99.55 29	99.37 85	99.64 8	98.05 31	99.53 14	96.58 34	98.93 41	99.92 28	99.49 18	99.46 14	99.32 11	99.80 30	99.64 120
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		99.38 6	99.60 3	99.12 9	99.76 2	99.62 33	99.39 30	98.23 18	99.52 16	98.03 17	99.45 12	99.98 2	99.64 5	99.58 8	99.30 12	99.68 99	99.76 64
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
3Dnovator+	96.92 7	98.71 37	99.05 45	98.32 33	99.53 30	99.34 92	99.06 46	94.61 59	99.65 6	97.49 24	96.75 105	99.86 38	99.44 23	98.78 62	99.30 12	99.81 23	99.67 109

QAPM		98.62 41	99.04 48	98.13 38	99.57 26	99.48 64	99.17 38	94.78 55	99.57 10	96.16 40	96.73 106	99.80 43	99.33 30	98.79 61	99.29 14	99.75 47	99.64 120

EC-MVSNet		98.22 52	99.44 17	96.79 75	95.62 128	99.56 51	99.01 50	92.22 105	99.17 57	94.51 70	99.41 14	99.62 52	99.49 18	99.16 34	99.26 15	99.91 2	99.94 1

APDe-MVS		99.49 1	99.64 1	99.32 2	99.74 4	99.74 12	99.75 1	98.34 4	99.56 11	98.72 6	99.57 8	99.97 8	99.53 15	99.65 2	99.25 16	99.84 12	99.77 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		99.30 9	99.54 7	99.03 16	99.66 16	99.64 27	99.68 4	98.25 14	99.56 11	97.12 30	99.19 22	99.95 17	99.72 1	99.43 16	99.25 16	99.72 69	99.77 58

HFP-MVS		99.32 8	99.53 9	99.07 13	99.69 7	99.59 45	99.63 12	98.31 9	99.56 11	97.37 26	99.27 20	99.97 8	99.70 3	99.35 22	99.24 18	99.71 79	99.76 64

UA-Net		97.13 86	99.14 39	94.78 120	97.21 80	99.38 81	97.56 114	92.04 108	98.48 133	88.03 137	98.39 68	99.91 31	94.03 197	99.33 24	99.23 19	99.81 23	99.25 166

LS3D		97.79 60	98.25 73	97.26 61	98.40 60	99.63 30	99.53 19	98.63 1	99.25 46	88.13 136	96.93 102	94.14 123	99.19 40	99.14 35	99.23 19	99.69 91	99.42 154

X-MVS		98.93 29	99.37 24	98.42 31	99.67 13	99.62 33	99.60 15	98.15 23	99.08 74	93.81 85	98.46 65	99.95 17	99.59 9	99.49 13	99.21 21	99.68 99	99.75 72

PGM-MVS		98.86 31	99.35 28	98.29 34	99.77 1	99.63 30	99.67 5	95.63 45	98.66 124	95.27 54	99.11 29	99.82 42	99.67 4	99.33 24	99.19 22	99.73 61	99.74 77

SteuartSystems-ACMMP		99.20 15	99.51 11	98.83 26	99.66 16	99.66 22	99.71 3	98.12 27	99.14 65	96.62 33	99.16 24	99.98 2	99.12 49	99.63 3	99.19 22	99.78 34	99.83 29
Skip Steuart: Steuart Systems R&D Blog.
test1111		97.09 88	96.83 135	97.39 55	96.92 88	99.81 3	98.44 72	94.45 65	99.17 57	95.85 44	92.10 172	88.97 158	98.78 73	99.02 43	99.11 24	99.88 4	99.63 122

test2506		97.16 84	96.68 139	97.73 47	96.95 86	99.79 4	98.48 68	94.42 66	99.17 57	97.74 22	99.15 25	80.93 209	98.89 68	99.03 41	99.09 25	99.88 4	99.62 124

ECVR-MVS		97.27 79	97.09 124	97.48 54	96.95 86	99.79 4	98.48 68	94.42 66	99.17 57	96.28 39	93.54 158	89.39 155	98.89 68	99.03 41	99.09 25	99.88 4	99.61 127

CS-MVS		98.56 44	99.32 29	97.68 48	98.28 63	99.89 2	98.71 61	94.53 64	99.41 23	95.43 50	99.05 36	98.66 65	99.19 40	99.21 29	99.07 27	99.93 1	99.94 1

TSAR-MVS + MP.		99.27 10	99.57 5	98.92 22	98.78 54	99.53 55	99.72 2	98.11 28	99.73 3	97.43 25	99.15 25	99.96 12	99.59 9	99.73 1	99.07 27	99.88 4	99.82 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SD-MVS		99.25 12	99.50 12	98.96 20	98.79 53	99.55 53	99.33 33	98.29 12	99.75 2	97.96 18	99.15 25	99.95 17	99.61 6	99.17 32	99.06 29	99.81 23	99.84 25
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
sasdasda		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 117	99.22 49	95.39 51	98.48 61	90.95 143	99.16 46	97.66 137	99.05 30	99.76 41	99.90 7

DVP-MVS		99.45 2	99.54 7	99.35 1	99.72 6	99.76 6	99.63 12	98.37 2	99.63 8	99.03 3	98.95 40	99.98 2	99.60 7	99.60 7	99.05 30	99.74 53	99.79 45
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSP-MVS		99.34 7	99.52 10	99.14 7	99.68 12	99.75 9	99.64 8	98.31 9	99.44 21	98.10 13	99.28 19	99.98 2	99.30 35	99.34 23	99.05 30	99.81 23	99.79 45
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
canonicalmvs		97.31 76	97.81 95	96.72 76	96.20 102	99.45 68	98.21 86	91.60 117	99.22 49	95.39 51	98.48 61	90.95 143	99.16 46	97.66 137	99.05 30	99.76 41	99.90 7

OpenMVS	96.23 11	97.95 58	98.45 67	97.35 56	99.52 32	99.42 77	98.91 53	94.61 59	98.87 97	92.24 113	94.61 147	99.05 64	99.10 51	98.64 73	99.05 30	99.74 53	99.51 146

MGCFI-Net		97.26 81	97.79 97	96.64 82	96.17 104	99.43 75	98.14 91	91.52 122	99.23 47	95.16 57	98.48 61	90.87 145	99.07 54	97.59 143	99.02 35	99.76 41	99.91 6

Vis-MVSNet		96.16 122	98.22 77	93.75 137	95.33 141	99.70 18	97.27 123	90.85 133	98.30 143	85.51 155	95.72 136	96.45 90	93.69 203	98.70 70	99.00 36	99.84 12	99.69 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		98.46 45	99.16 38	97.64 50	98.48 59	99.64 27	99.35 32	94.71 57	99.53 14	95.17 56	97.63 89	99.59 54	98.38 93	98.88 57	98.99 37	99.74 53	99.86 21

CDPH-MVS		98.41 46	99.10 41	97.61 51	99.32 42	99.36 87	99.49 22	96.15 44	98.82 107	91.82 116	98.41 66	99.66 51	99.10 51	98.93 50	98.97 38	99.75 47	99.58 129

DPE-MVS		99.39 5	99.55 6	99.20 4	99.63 20	99.71 16	99.66 6	98.33 6	99.29 39	98.40 11	99.64 6	99.98 2	99.31 33	99.56 9	98.96 39	99.85 10	99.70 98
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + ACMM		98.77 34	99.45 14	97.98 43	99.37 37	99.46 66	99.44 28	98.13 26	99.65 6	92.30 111	98.91 43	99.95 17	99.05 55	99.42 17	98.95 40	99.58 147	99.82 30

EPP-MVSNet		97.75 63	98.71 60	96.63 83	95.68 124	99.56 51	97.51 115	93.10 101	99.22 49	94.99 61	97.18 97	97.30 84	98.65 82	98.83 59	98.93 41	99.84 12	99.92 3

CHOSEN 280x420		97.99 57	99.24 35	96.53 85	98.34 61	99.61 38	98.36 79	89.80 151	99.27 42	95.08 59	99.81 1	98.58 68	98.64 83	99.02 43	98.92 42	98.93 196	99.48 150

CSCG		98.90 30	98.93 53	98.85 24	99.75 3	99.72 13	99.49 22	96.58 42	99.38 25	98.05 16	98.97 38	97.87 77	99.49 18	97.78 130	98.92 42	99.78 34	99.90 7

CHOSEN 1792x2688		96.41 115	96.99 130	95.74 109	98.01 67	99.72 13	97.70 110	90.78 136	99.13 69	90.03 129	87.35 205	95.36 106	98.33 94	98.59 81	98.91 44	99.59 143	99.87 18

MVS_111021_LR		98.67 38	99.41 22	97.81 46	99.37 37	99.53 55	98.51 67	95.52 48	99.27 42	94.85 62	99.56 9	99.69 50	99.04 56	99.36 20	98.88 45	99.60 137	99.58 129

DVP-MVS++		99.41 4	99.64 1	99.14 7	99.69 7	99.75 9	99.64 8	98.33 6	99.67 5	98.10 13	99.66 5	99.99 1	99.33 30	99.62 5	98.86 46	99.74 53	99.90 7

CP-MVS		99.27 10	99.44 17	99.08 12	99.62 22	99.58 48	99.53 19	98.16 21	99.21 52	97.79 20	99.15 25	99.96 12	99.59 9	99.54 11	98.86 46	99.78 34	99.74 77

MAR-MVS		97.71 64	98.04 85	97.32 57	99.35 41	98.91 121	97.65 112	91.68 115	98.00 155	97.01 31	97.72 87	94.83 113	98.85 71	98.44 90	98.86 46	99.41 177	99.52 141
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
SPE-MVS-test		98.58 43	99.42 21	97.60 52	98.52 58	99.91 1	98.60 64	94.60 61	99.37 27	94.62 66	99.40 15	99.16 61	99.39 26	99.36 20	98.85 49	99.90 3	99.92 3

SF-MVS		99.18 16	99.32 29	99.03 16	99.65 18	99.41 79	98.87 54	98.24 17	99.14 65	98.73 5	99.11 29	99.92 28	98.92 62	99.22 28	98.84 50	99.76 41	99.56 135

SED-MVS		99.44 3	99.58 4	99.28 3	99.69 7	99.76 6	99.62 14	98.35 3	99.51 17	99.05 2	99.60 7	99.98 2	99.28 37	99.61 6	98.83 51	99.70 87	99.77 58

MVS_111021_HR		98.59 42	99.36 25	97.68 48	99.42 35	99.61 38	98.14 91	94.81 54	99.31 36	95.00 60	99.51 10	99.79 45	99.00 59	98.94 49	98.83 51	99.69 91	99.57 134

CNLPA		99.03 27	99.05 45	99.01 19	99.27 43	99.22 107	99.03 48	97.98 32	99.34 34	99.00 4	98.25 72	99.71 49	99.31 33	98.80 60	98.82 53	99.48 166	99.17 170

FMVSNet2		96.64 108	97.50 104	95.63 112	93.81 161	97.98 171	98.09 94	90.87 132	98.99 86	93.48 92	93.17 165	95.25 108	97.89 109	98.63 74	98.80 54	99.68 99	99.67 109

MP-MVS		99.07 23	99.36 25	98.74 27	99.63 20	99.57 50	99.66 6	98.25 14	99.00 85	95.62 46	98.97 38	99.94 25	99.54 14	99.51 12	98.79 55	99.71 79	99.73 83
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		98.05 55	99.25 34	96.65 80	95.61 129	99.61 38	98.26 85	93.52 85	98.90 96	93.74 89	99.32 18	99.20 59	98.90 65	99.21 29	98.72 56	99.87 8	99.79 45

TSAR-MVS + GP.		98.66 40	99.36 25	97.85 45	97.16 82	99.46 66	99.03 48	94.59 62	99.09 72	97.19 29	99.73 3	99.95 17	99.39 26	98.95 48	98.69 57	99.75 47	99.65 116

ACMMP_NAP		99.05 25	99.45 14	98.58 30	99.73 5	99.60 43	99.64 8	98.28 13	99.23 47	94.57 67	99.35 17	99.97 8	99.55 13	99.63 3	98.66 58	99.70 87	99.74 77

OMC-MVS		98.84 32	99.01 50	98.65 29	99.39 36	99.23 106	99.22 35	96.70 41	99.40 24	97.77 21	97.89 82	99.80 43	99.21 38	99.02 43	98.65 59	99.57 151	99.07 177

FMVSNet3		97.02 90	98.12 82	95.73 110	93.59 167	97.98 171	98.34 81	91.32 126	98.80 110	93.92 81	97.21 94	95.94 102	97.63 118	98.61 76	98.62 60	99.61 129	99.65 116

CNVR-MVS		99.23 14	99.28 32	99.17 5	99.65 18	99.34 92	99.46 25	98.21 19	99.28 40	98.47 8	98.89 45	99.94 25	99.50 16	99.42 17	98.61 61	99.73 61	99.52 141

baseline		97.45 73	98.70 61	95.99 104	95.89 110	99.36 87	98.29 82	91.37 125	99.21 52	92.99 99	98.40 67	96.87 89	97.96 107	98.60 79	98.60 62	99.42 176	99.86 21

MVS_Test		97.30 78	98.54 63	95.87 106	95.74 117	99.28 99	98.19 88	91.40 124	99.18 56	91.59 118	98.17 74	96.18 97	98.63 84	98.61 76	98.55 63	99.66 113	99.78 51

EPNet		98.05 55	98.86 55	97.10 64	99.02 48	99.43 75	98.47 70	94.73 56	99.05 80	95.62 46	98.93 41	97.62 81	95.48 174	98.59 81	98.55 63	99.29 186	99.84 25
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		95.33 140	97.09 124	93.27 152	95.23 142	98.39 160	95.49 166	92.58 104	97.71 170	83.00 170	94.44 151	93.28 131	93.92 200	97.79 129	98.54 65	99.41 177	99.45 152

casdiffmvs		96.93 93	97.43 110	96.34 92	95.70 121	99.50 62	97.75 108	93.22 96	98.98 87	92.64 104	94.97 143	91.71 140	98.93 61	98.62 75	98.52 66	99.82 16	99.72 93
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		97.41 74	98.49 66	96.15 96	97.49 72	99.76 6	96.02 157	93.75 81	99.26 44	93.38 94	93.73 156	99.35 57	96.47 149	98.96 47	98.46 67	99.77 39	99.90 7

casdiffmvs_mvg		97.27 79	97.97 90	96.46 89	95.83 114	99.51 61	98.42 73	93.32 91	98.34 141	92.38 109	95.64 137	95.35 107	98.91 63	98.73 68	98.45 68	99.86 9	99.80 37
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DCV-MVSNet		97.56 69	98.36 69	96.62 84	96.44 93	98.36 162	98.37 77	91.73 114	99.11 70	94.80 63	98.36 69	96.28 95	98.60 86	98.12 103	98.44 69	99.76 41	99.87 18

baseline1		97.58 68	98.05 84	97.02 69	96.21 101	99.45 68	97.71 109	93.71 83	98.47 134	95.75 45	98.78 49	93.20 133	98.91 63	98.52 85	98.44 69	99.81 23	99.53 138

NCCC		99.05 25	99.08 42	99.02 18	99.62 22	99.38 81	99.43 29	98.21 19	99.36 30	97.66 23	97.79 83	99.90 33	99.45 22	99.17 32	98.43 71	99.77 39	99.51 146

PVSNet_BlendedMVS		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 121	95.02 52	99.08 74	95.51 48	98.05 76	90.11 149	98.07 103	98.91 53	98.40 72	99.72 69	99.78 51

PVSNet_Blended		97.51 71	97.71 98	97.28 59	98.06 65	99.61 38	97.31 121	95.02 52	99.08 74	95.51 48	98.05 76	90.11 149	98.07 103	98.91 53	98.40 72	99.72 69	99.78 51

train_agg		98.73 36	99.11 40	98.28 35	99.36 39	99.35 90	99.48 24	97.96 33	98.83 105	93.86 84	98.70 55	99.86 38	99.44 23	99.08 39	98.38 74	99.61 129	99.58 129

CDS-MVSNet		96.59 111	98.02 87	94.92 119	94.45 154	98.96 119	97.46 117	91.75 113	97.86 164	90.07 128	96.02 124	97.25 85	96.21 153	98.04 114	98.38 74	99.60 137	99.65 116
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HPM-MVS++		99.10 21	99.30 31	98.86 23	99.69 7	99.48 64	99.59 16	98.34 4	99.26 44	96.55 36	99.10 31	99.96 12	99.36 28	99.25 27	98.37 76	99.64 121	99.66 113

MCST-MVS		99.11 20	99.27 33	98.93 21	99.67 13	99.33 95	99.51 21	98.31 9	99.28 40	96.57 35	99.10 31	99.90 33	99.71 2	99.19 31	98.35 77	99.82 16	99.71 96

MSDG		98.27 51	98.29 71	98.24 36	99.20 44	99.22 107	99.20 36	97.82 35	99.37 27	94.43 73	95.90 128	97.31 83	99.12 49	98.76 64	98.35 77	99.67 108	99.14 174

test0.0.03 1		96.69 104	98.12 82	95.01 118	95.49 136	98.99 116	95.86 159	90.82 134	98.38 137	92.54 108	96.66 109	97.33 82	95.75 164	97.75 133	98.34 79	99.60 137	99.40 157

GBi-Net		96.98 91	98.00 88	95.78 107	93.81 161	97.98 171	98.09 94	91.32 126	98.80 110	93.92 81	97.21 94	95.94 102	97.89 109	98.07 109	98.34 79	99.68 99	99.67 109

test1		96.98 91	98.00 88	95.78 107	93.81 161	97.98 171	98.09 94	91.32 126	98.80 110	93.92 81	97.21 94	95.94 102	97.89 109	98.07 109	98.34 79	99.68 99	99.67 109

FMVSNet1		95.77 130	96.41 154	95.03 117	93.42 169	97.86 178	97.11 134	89.89 148	98.53 131	92.00 114	89.17 190	93.23 132	98.15 100	98.07 109	98.34 79	99.61 129	99.69 102

diffmvs_AUTHOR		96.68 106	97.10 123	96.19 95	95.71 119	99.37 85	97.91 100	93.19 99	99.36 30	91.97 115	95.90 128	89.02 157	98.67 81	98.01 117	98.30 83	99.68 99	99.74 77

viewmanbaseed2359cas		96.92 94	97.60 102	96.14 97	95.71 119	99.44 74	97.82 103	93.39 87	98.93 92	91.34 120	96.10 122	92.27 136	98.82 72	98.40 92	98.30 83	99.75 47	99.75 72

EIA-MVS		97.70 65	98.78 58	96.44 90	95.72 118	99.65 23	98.14 91	93.72 82	98.30 143	92.31 110	98.63 56	97.90 76	98.97 60	98.92 52	98.30 83	99.78 34	99.80 37

UGNet		97.66 66	99.07 44	96.01 103	97.19 81	99.65 23	97.09 135	93.39 87	99.35 32	94.40 75	98.79 48	99.59 54	94.24 194	98.04 114	98.29 86	99.73 61	99.80 37
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
viewmacassd2359aftdt		96.50 113	97.01 129	95.91 105	95.65 126	99.45 68	97.65 112	93.31 92	98.36 139	90.30 126	94.48 150	90.82 146	98.77 74	97.91 122	98.26 87	99.76 41	99.77 58

IterMVS-LS		96.12 123	97.48 106	94.53 123	95.19 143	97.56 196	97.15 131	89.19 158	99.08 74	88.23 135	94.97 143	94.73 115	97.84 114	97.86 127	98.26 87	99.60 137	99.88 16
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous202405211			97.40 111		96.45 92	99.54 54	98.08 97	93.79 78	98.24 147		93.55 157	94.41 119	98.88 70	98.04 114	98.24 89	99.75 47	99.76 64

EPNet_dtu		96.30 118	98.53 64	93.70 140	98.97 49	98.24 166	97.36 119	94.23 70	98.85 100	79.18 192	99.19 22	98.47 70	94.09 196	97.89 125	98.21 90	98.39 202	98.85 186
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CPTT-MVS		99.14 19	99.20 37	99.06 14	99.58 25	99.53 55	99.45 26	97.80 36	99.19 55	98.32 12	98.58 58	99.95 17	99.60 7	99.28 26	98.20 91	99.64 121	99.69 102

HyFIR lowres test		95.99 126	96.56 141	95.32 115	97.99 68	99.65 23	96.54 146	88.86 160	98.44 135	89.77 132	84.14 215	97.05 87	99.03 57	98.55 83	98.19 92	99.73 61	99.86 21

diffmvs		96.83 96	97.33 114	96.25 93	95.76 116	99.34 92	98.06 98	93.22 96	99.43 22	92.30 111	96.90 103	89.83 154	98.55 88	98.00 118	98.14 93	99.64 121	99.70 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TAPA-MVS	97.53 5	98.41 46	98.84 57	97.91 44	99.08 47	99.33 95	99.15 39	97.13 40	99.34 34	93.20 95	97.75 85	99.19 60	99.20 39	98.66 71	98.13 94	99.66 113	99.48 150
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	97.93 2	99.02 28	98.94 52	99.11 10	99.46 34	99.24 104	99.06 46	97.96 33	99.31 36	99.16 1	97.90 81	99.79 45	99.36 28	98.71 69	98.12 95	99.65 117	99.52 141
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DPM-MVS		98.31 50	98.53 64	98.05 40	98.76 55	98.77 129	99.13 40	98.07 29	99.10 71	94.27 78	96.70 107	99.84 41	98.70 77	97.90 124	98.11 96	99.40 179	99.28 162

Anonymous20231211		97.10 87	97.06 127	97.14 63	96.32 95	99.52 58	98.16 89	93.76 79	98.84 104	95.98 42	90.92 178	94.58 118	98.90 65	97.72 135	98.10 97	99.71 79	99.75 72

gg-mvs-nofinetune		90.85 204	94.14 183	87.02 209	94.89 149	99.25 102	98.64 62	76.29 224	88.24 225	57.50 229	79.93 221	95.45 105	95.18 183	98.77 63	98.07 98	99.62 127	99.24 167

CANet_DTU		96.64 108	99.08 42	93.81 136	97.10 83	99.42 77	98.85 55	90.01 145	99.31 36	79.98 188	99.78 2	99.10 63	97.42 124	98.35 93	98.05 99	99.47 168	99.53 138

Fast-Effi-MVS+		95.38 138	96.52 144	94.05 133	94.15 156	99.14 111	97.24 126	86.79 181	98.53 131	87.62 142	94.51 148	87.06 165	98.76 75	98.60 79	98.04 100	99.72 69	99.77 58

GG-mvs-BLEND		69.11 222	98.13 81	35.26 226	3.49 236	98.20 168	94.89 177	2.38 232	98.42 136	5.82 237	96.37 118	98.60 67	5.97 232	98.75 66	97.98 101	99.01 194	98.61 188

Effi-MVS+		95.81 129	97.31 118	94.06 132	95.09 144	99.35 90	97.24 126	88.22 169	98.54 130	85.38 156	98.52 59	88.68 159	98.70 77	98.32 94	97.93 102	99.74 53	99.84 25

GeoE		95.98 128	97.24 120	94.51 124	95.02 146	99.38 81	98.02 99	87.86 175	98.37 138	87.86 140	92.99 170	93.54 128	98.56 87	98.61 76	97.92 103	99.73 61	99.85 24

MIMVSNet		94.49 158	97.59 103	90.87 191	91.74 193	98.70 138	94.68 186	78.73 218	97.98 156	83.71 164	97.71 88	94.81 114	96.96 133	97.97 119	97.92 103	99.40 179	98.04 200

DI_MVS_pp		96.90 95	97.49 105	96.21 94	95.61 129	99.40 80	98.72 60	92.11 106	99.14 65	92.98 100	93.08 168	95.14 109	98.13 101	98.05 113	97.91 105	99.74 53	99.73 83

testgi		95.67 132	97.48 106	93.56 143	95.07 145	99.00 114	95.33 170	88.47 166	98.80 110	86.90 146	97.30 93	92.33 135	95.97 161	97.66 137	97.91 105	99.60 137	99.38 158

thres100view900		96.72 102	96.47 149	97.00 72	96.31 96	99.52 58	98.28 83	94.01 72	97.35 176	94.52 68	95.90 128	86.93 168	99.09 53	98.07 109	97.87 107	99.81 23	99.63 122

viewmambaseed2359dif		96.82 97	97.19 121	96.39 91	95.64 127	99.38 81	98.15 90	93.24 93	98.78 116	92.85 102	95.93 127	91.24 142	98.75 76	97.41 149	97.86 108	99.70 87	99.74 77

COLMAP_ROB	96.15 12	97.78 61	98.17 79	97.32 57	98.84 50	99.45 68	99.28 34	95.43 49	99.48 19	91.80 117	94.83 146	98.36 72	98.90 65	98.09 106	97.85 109	99.68 99	99.15 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AdaColmap		99.06 24	98.98 51	99.15 6	99.60 24	99.30 98	99.38 31	98.16 21	99.02 83	98.55 7	98.71 54	99.57 56	99.58 12	99.09 37	97.84 110	99.64 121	99.36 159

thres200		96.76 99	96.53 143	97.03 67	96.31 96	99.67 19	98.37 77	93.99 74	97.68 171	94.49 71	95.83 133	86.77 170	99.18 43	98.26 96	97.82 111	99.82 16	99.66 113

tfpn200view9		96.75 100	96.51 145	97.03 67	96.31 96	99.67 19	98.41 74	93.99 74	97.35 176	94.52 68	95.90 128	86.93 168	99.14 48	98.26 96	97.80 112	99.82 16	99.70 98

thres400		96.71 103	96.45 151	97.02 69	96.28 99	99.63 30	98.41 74	94.00 73	97.82 166	94.42 74	95.74 134	86.26 176	99.18 43	98.20 100	97.79 113	99.81 23	99.70 98

FC-MVSNet-train		97.04 89	97.91 92	96.03 102	96.00 107	98.41 158	96.53 148	93.42 86	99.04 82	93.02 98	98.03 78	94.32 121	97.47 123	97.93 121	97.77 114	99.75 47	99.88 16

baseline2		96.36 117	97.82 94	94.65 122	94.60 153	99.09 112	96.45 150	89.63 153	98.36 139	91.29 122	97.60 90	94.13 124	96.37 150	98.45 88	97.70 115	99.54 160	99.41 155

IterMVS-SCA-FT		94.89 147	97.87 93	91.42 179	94.86 150	97.70 182	97.24 126	84.88 195	98.93 92	75.74 204	94.26 152	98.25 73	96.69 140	98.52 85	97.68 116	99.10 193	99.73 83

viewmsd2359difaftdt		96.47 114	96.78 136	96.11 99	95.69 122	99.24 104	97.16 130	93.19 99	99.35 32	92.93 101	95.88 132	89.34 156	98.69 79	96.31 182	97.65 117	98.99 195	99.68 107

thres600view7		96.69 104	96.43 153	97.00 72	96.28 99	99.67 19	98.41 74	93.99 74	97.85 165	94.29 77	95.96 125	85.91 179	99.19 40	98.26 96	97.63 118	99.82 16	99.73 83

PMMVS		97.52 70	98.39 68	96.51 87	95.82 115	98.73 136	97.80 105	93.05 102	98.76 118	94.39 76	99.07 34	97.03 88	98.55 88	98.31 95	97.61 119	99.43 174	99.21 169

IterMVS		94.81 149	97.71 98	91.42 179	94.83 151	97.63 189	97.38 118	85.08 192	98.93 92	75.67 205	94.02 153	97.64 79	96.66 143	98.45 88	97.60 120	98.90 197	99.72 93
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		95.74 131	98.04 85	93.06 154	93.92 157	99.16 109	97.90 101	88.16 171	99.07 79	82.02 176	98.02 79	94.32 121	96.74 139	98.53 84	97.56 121	99.61 129	99.62 124

gm-plane-assit		89.44 211	92.82 208	85.49 213	91.37 206	95.34 220	79.55 228	82.12 202	91.68 224	64.79 226	87.98 201	80.26 213	95.66 167	98.51 87	97.56 121	99.45 170	98.41 193

LGP-MVS_train		96.23 119	96.89 132	95.46 114	97.32 76	98.77 129	98.81 57	93.60 84	98.58 127	85.52 154	99.08 33	86.67 172	97.83 115	97.87 126	97.51 123	99.69 91	99.73 83

ACMMP		98.74 35	99.03 49	98.40 32	99.36 39	99.64 27	99.20 36	97.75 37	98.82 107	95.24 55	98.85 46	99.87 37	99.17 45	98.74 67	97.50 124	99.71 79	99.76 64
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CR-MVSNet		94.57 157	97.34 113	91.33 182	94.90 148	98.59 145	97.15 131	79.14 214	97.98 156	80.42 184	96.59 114	93.50 130	96.85 136	98.10 104	97.49 125	99.50 165	99.15 171

PatchT		93.96 166	97.36 112	90.00 198	94.76 152	98.65 140	90.11 214	78.57 219	97.96 159	80.42 184	96.07 123	94.10 125	96.85 136	98.10 104	97.49 125	99.26 187	99.15 171

FC-MVSNet-test		96.07 124	97.94 91	93.89 134	93.60 166	98.67 139	96.62 145	90.30 144	98.76 118	88.62 133	95.57 140	97.63 80	94.48 190	97.97 119	97.48 127	99.71 79	99.52 141

UniMVSNet_ETH3D		93.15 177	92.33 210	94.11 131	93.91 158	98.61 144	94.81 181	90.98 131	97.06 185	87.51 143	82.27 219	76.33 225	97.87 113	94.79 207	97.47 128	99.56 154	99.81 35

PCF-MVS	97.50 6	98.18 54	98.35 70	97.99 42	98.65 56	99.36 87	98.94 52	98.14 25	98.59 126	93.62 90	96.61 111	99.76 48	99.03 57	97.77 131	97.45 129	99.57 151	98.89 185
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PatchMatch-RL		97.77 62	98.25 73	97.21 62	99.11 46	99.25 102	97.06 137	94.09 71	98.72 122	95.14 58	98.47 64	96.29 94	98.43 92	98.65 72	97.44 130	99.45 170	98.94 180

TAMVS		95.53 134	96.50 147	94.39 128	93.86 160	99.03 113	96.67 143	89.55 155	97.33 178	90.64 124	93.02 169	91.58 141	96.21 153	97.72 135	97.43 131	99.43 174	99.36 159

LTVRE_ROB	93.20 16	92.84 182	94.92 169	90.43 195	92.83 171	98.63 141	97.08 136	87.87 174	97.91 161	68.42 222	93.54 158	79.46 219	96.62 144	97.55 145	97.40 132	99.74 53	99.92 3
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
MVSTER		97.16 84	97.71 98	96.52 86	95.97 109	98.48 151	98.63 63	92.10 107	98.68 123	95.96 43	99.23 21	91.79 139	96.87 135	98.76 64	97.37 133	99.57 151	99.68 107

Baseline_NR-MVSNet		93.87 168	93.98 190	93.75 137	91.66 195	97.02 209	95.53 165	91.52 122	97.16 184	87.77 141	87.93 203	83.69 191	96.35 151	95.10 203	97.23 134	99.68 99	99.73 83

FMVSNet5		95.42 136	96.47 149	94.20 129	92.26 181	95.99 217	95.66 162	87.15 179	97.87 163	93.46 93	96.68 108	93.79 127	97.52 120	97.10 163	97.21 135	99.11 192	96.62 217

pm-mvs1		94.27 159	95.57 164	92.75 157	92.58 174	98.13 169	94.87 179	90.71 138	96.70 195	83.78 161	89.94 186	89.85 153	94.96 187	97.58 144	97.07 136	99.61 129	99.72 93

Fast-Effi-MVS+-dtu		95.38 138	98.20 78	92.09 165	93.91 158	98.87 123	97.35 120	85.01 194	99.08 74	81.09 180	98.10 75	96.36 93	95.62 169	98.43 91	97.03 137	99.55 156	99.50 148

TransMVSNet (Re)		93.45 173	94.08 186	92.72 158	92.83 171	97.62 192	94.94 175	91.54 121	95.65 213	83.06 169	88.93 193	83.53 193	94.25 193	97.41 149	97.03 137	99.67 108	98.40 196

DU-MVS		93.98 165	94.44 180	93.44 147	91.66 195	97.77 179	95.03 172	91.57 119	97.17 182	86.12 148	93.13 166	81.13 208	96.60 145	95.10 203	97.01 139	99.67 108	99.80 37

TSAR-MVS + COLMAP		96.79 98	96.55 142	97.06 65	97.70 71	98.46 153	99.07 45	96.23 43	99.38 25	91.32 121	98.80 47	85.61 181	98.69 79	97.64 141	96.92 140	99.37 181	99.06 178

CLD-MVS		96.74 101	96.51 145	97.01 71	96.71 90	98.62 142	98.73 59	94.38 68	98.94 90	94.46 72	97.33 92	87.03 166	98.07 103	97.20 159	96.87 141	99.72 69	99.54 137
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		93.67 171	94.14 183	93.13 153	91.28 209	97.58 194	95.60 164	91.97 110	97.06 185	84.05 157	90.64 183	82.22 203	96.17 156	94.94 206	96.78 142	99.69 91	99.78 51

RPMNet		94.66 151	97.16 122	91.75 175	94.98 147	98.59 145	97.00 138	78.37 220	97.98 156	83.78 161	96.27 119	94.09 126	96.91 134	97.36 152	96.73 143	99.48 166	99.09 176

UniMVSNet_NR-MVSNet		94.59 155	95.47 165	93.55 144	91.85 190	97.89 177	95.03 172	92.00 109	97.33 178	86.12 148	93.19 164	87.29 164	96.60 145	96.12 187	96.70 144	99.72 69	99.80 37

ET-MVSNet_ETH3D		96.17 121	96.99 130	95.21 116	88.53 219	98.54 148	98.28 83	92.61 103	98.85 100	93.60 91	99.06 35	90.39 148	98.63 84	95.98 192	96.68 145	99.61 129	99.41 155

ACMH	95.42 14	95.27 141	95.96 158	94.45 126	96.83 89	98.78 128	94.72 184	91.67 116	98.95 88	86.82 147	96.42 117	83.67 192	97.00 131	97.48 148	96.68 145	99.69 91	99.76 64
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		96.22 120	95.85 162	96.65 80	97.75 69	98.54 148	99.00 51	95.53 46	96.88 189	89.88 130	95.95 126	86.46 175	98.07 103	97.65 140	96.63 147	99.67 108	98.83 187
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMP	96.25 10	96.62 110	96.72 137	96.50 88	96.96 85	98.75 133	97.80 105	94.30 69	98.85 100	93.12 97	98.78 49	86.61 173	97.23 128	97.73 134	96.61 148	99.62 127	99.71 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
dmvs_re		96.02 125	96.49 148	95.47 113	93.49 168	99.26 101	97.25 125	93.82 77	97.51 173	90.43 125	97.52 91	87.93 161	98.12 102	96.86 167	96.59 149	99.73 61	99.76 64

ACMH+	95.51 13	95.40 137	96.00 156	94.70 121	96.33 94	98.79 126	96.79 141	91.32 126	98.77 117	87.18 144	95.60 139	85.46 182	96.97 132	97.15 160	96.59 149	99.59 143	99.65 116

CP-MVSNet		93.25 176	94.00 189	92.38 160	91.65 197	97.56 196	94.38 193	89.20 157	96.05 207	83.16 168	89.51 188	81.97 204	96.16 157	96.43 176	96.56 151	99.71 79	99.89 13

HQP-MVS		96.37 116	96.58 140	96.13 98	97.31 78	98.44 155	98.45 71	95.22 50	98.86 98	88.58 134	98.33 70	87.00 167	97.67 117	97.23 157	96.56 151	99.56 154	99.62 124

FA-MVS(training)		96.52 112	98.29 71	94.45 126	95.88 112	99.52 58	97.66 111	81.47 203	98.94 90	93.79 88	95.54 141	99.11 62	98.29 95	98.89 55	96.49 153	99.63 126	99.52 141

PS-CasMVS		92.72 187	93.36 201	91.98 169	91.62 199	97.52 198	94.13 197	88.98 159	95.94 210	81.51 179	87.35 205	79.95 216	95.91 162	96.37 178	96.49 153	99.70 87	99.89 13

Anonymous20231206		90.70 206	93.93 191	86.92 210	90.21 216	96.79 212	90.30 213	86.61 185	96.05 207	69.25 220	88.46 197	84.86 188	85.86 218	97.11 162	96.47 155	99.30 185	97.80 204

MVS-HIRNet		92.51 190	95.97 157	88.48 206	93.73 164	98.37 161	90.33 212	75.36 226	98.32 142	77.78 198	89.15 191	94.87 112	95.14 184	97.62 142	96.39 156	98.51 199	97.11 210

DTE-MVSNet		92.42 195	92.85 206	91.91 172	90.87 212	96.97 210	94.53 192	89.81 149	95.86 212	81.59 178	88.83 194	77.88 223	95.01 186	94.34 210	96.35 157	99.64 121	99.73 83

ACMM	96.26 9	96.67 107	96.69 138	96.66 79	97.29 79	98.46 153	96.48 149	95.09 51	99.21 52	93.19 96	98.78 49	86.73 171	98.17 97	97.84 128	96.32 158	99.74 53	99.49 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EU-MVSNet		92.80 184	94.76 174	90.51 193	91.88 188	96.74 214	92.48 204	88.69 163	96.21 202	79.00 193	91.51 174	87.82 162	91.83 212	95.87 194	96.27 159	99.21 188	98.92 184

PEN-MVS		92.72 187	93.20 203	92.15 164	91.29 207	97.31 206	94.67 187	89.81 149	96.19 203	81.83 177	88.58 196	79.06 220	95.61 170	95.21 200	96.27 159	99.72 69	99.82 30

TinyColmap		94.00 164	94.35 181	93.60 141	95.89 110	98.26 164	97.49 116	88.82 161	98.56 129	83.21 167	91.28 177	80.48 212	96.68 141	97.34 153	96.26 161	99.53 162	98.24 197

test-mter		94.86 148	97.32 115	92.00 168	92.41 178	98.82 125	96.18 156	86.35 187	98.05 153	82.28 174	96.48 116	94.39 120	95.46 176	98.17 102	96.20 162	99.32 184	99.13 175

NR-MVSNet		94.01 163	94.51 178	93.44 147	92.56 175	97.77 179	95.67 161	91.57 119	97.17 182	85.84 151	93.13 166	80.53 211	95.29 180	97.01 164	96.17 163	99.69 91	99.75 72

tfpnnormal		93.85 170	94.12 185	93.54 145	93.22 170	98.24 166	95.45 167	91.96 111	94.61 216	83.91 159	90.74 180	81.75 206	97.04 130	97.49 147	96.16 164	99.68 99	99.84 25

USDC		94.26 160	94.83 172	93.59 142	96.02 105	98.44 155	97.84 102	88.65 164	98.86 98	82.73 173	94.02 153	80.56 210	96.76 138	97.28 156	96.15 165	99.55 156	98.50 191

thisisatest0530		97.23 82	98.25 73	96.05 100	95.60 131	99.59 45	96.96 139	93.23 94	99.17 57	92.60 106	98.75 52	96.19 96	98.17 97	98.19 101	96.10 166	99.72 69	99.77 58

tttt0517		97.23 82	98.24 76	96.04 101	95.60 131	99.60 43	96.94 140	93.23 94	99.15 62	92.56 107	98.74 53	96.12 99	98.17 97	98.21 99	96.10 166	99.73 61	99.78 51

test-LLR		95.50 135	97.32 115	93.37 149	95.49 136	98.74 134	96.44 151	90.82 134	98.18 148	82.75 171	96.60 112	94.67 116	95.54 172	98.09 106	96.00 168	99.20 189	98.93 181

TESTMET0.1,1		94.95 145	97.32 115	92.20 163	92.62 173	98.74 134	96.44 151	86.67 183	98.18 148	82.75 171	96.60 112	94.67 116	95.54 172	98.09 106	96.00 168	99.20 189	98.93 181

EG-PatchMatch MVS		92.45 191	93.92 192	90.72 192	92.56 175	98.43 157	94.88 178	84.54 197	97.18 181	79.55 190	86.12 212	83.23 196	93.15 207	97.22 158	96.00 168	99.67 108	99.27 165

UniMVSNet (Re)		94.58 156	95.34 166	93.71 139	92.25 182	98.08 170	94.97 174	91.29 130	97.03 187	87.94 138	93.97 155	86.25 177	96.07 158	96.27 184	95.97 171	99.72 69	99.79 45

anonymousdsp		93.12 178	95.86 161	89.93 200	91.09 210	98.25 165	95.12 171	85.08 192	97.44 175	73.30 212	90.89 179	90.78 147	95.25 182	97.91 122	95.96 172	99.71 79	99.82 30

WR-MVS_H		93.54 172	94.67 176	92.22 161	91.95 186	97.91 176	94.58 190	88.75 162	96.64 196	83.88 160	90.66 182	85.13 185	94.40 191	96.54 174	95.91 173	99.73 61	99.89 13

WR-MVS		93.43 175	94.48 179	92.21 162	91.52 202	97.69 184	94.66 188	89.98 146	96.86 190	83.43 165	90.12 184	85.03 186	93.94 199	96.02 191	95.82 174	99.71 79	99.82 30

IB-MVS	93.96 15	95.02 144	96.44 152	93.36 150	97.05 84	99.28 99	90.43 211	93.39 87	98.02 154	96.02 41	94.92 145	92.07 138	83.52 220	95.38 197	95.82 174	99.72 69	99.59 128
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
pmmvs6		91.90 202	92.53 209	91.17 185	91.81 191	97.63 189	93.23 199	88.37 168	93.43 221	80.61 182	77.32 223	87.47 163	94.12 195	96.58 172	95.72 176	98.88 198	99.53 138

MS-PatchMatch		95.99 126	97.26 119	94.51 124	97.46 73	98.76 132	97.27 123	86.97 180	99.09 72	89.83 131	93.51 160	97.78 78	96.18 155	97.53 146	95.71 177	99.35 182	98.41 193

MDTV_nov1_ep13		95.57 133	97.48 106	93.35 151	95.43 138	98.97 118	97.19 129	83.72 201	98.92 95	87.91 139	97.75 85	96.12 99	97.88 112	96.84 169	95.64 178	97.96 207	98.10 199

MIMVSNet1		88.61 212	90.68 214	86.19 212	81.56 226	95.30 221	87.78 220	85.98 189	94.19 219	72.30 218	78.84 222	78.90 221	90.06 213	96.59 171	95.47 179	99.46 169	95.49 219

RPSCF		97.61 67	98.16 80	96.96 74	98.10 64	99.00 114	98.84 56	93.76 79	99.45 20	94.78 64	99.39 16	99.31 58	98.53 90	96.61 170	95.43 180	97.74 209	97.93 203

pmmvs4		95.09 142	95.90 159	94.14 130	92.29 180	97.70 182	95.45 167	90.31 142	98.60 125	90.70 123	93.25 163	89.90 152	96.67 142	97.13 161	95.42 181	99.44 172	99.28 162

GA-MVS		93.93 167	96.31 155	91.16 186	93.61 165	98.79 126	95.39 169	90.69 139	98.25 146	73.28 213	96.15 121	88.42 160	94.39 192	97.76 132	95.35 182	99.58 147	99.45 152

v10		92.79 185	94.06 187	91.31 183	91.78 192	97.29 208	94.87 179	86.10 188	96.97 188	79.82 189	88.16 199	84.56 189	95.63 168	96.33 181	95.31 183	99.65 117	99.80 37

v1192		92.43 194	93.61 196	91.05 187	91.53 201	97.43 202	94.61 189	87.99 173	96.60 197	76.72 200	87.11 207	82.74 201	95.85 163	96.35 180	95.30 184	99.60 137	99.74 77

test_method		87.27 215	91.58 211	82.25 217	75.65 230	87.52 229	86.81 222	72.60 227	97.51 173	73.20 214	85.07 214	79.97 215	88.69 215	97.31 154	95.24 185	96.53 221	98.41 193

v1144		92.81 183	94.03 188	91.40 181	91.68 194	97.60 193	94.73 183	88.40 167	96.71 194	78.48 195	88.14 200	84.46 190	95.45 177	96.31 182	95.22 186	99.65 117	99.76 64

v1240		91.99 201	93.33 202	90.44 194	91.29 207	97.30 207	94.25 195	86.79 181	96.43 200	75.49 207	86.34 211	81.85 205	95.29 180	96.42 177	95.22 186	99.52 163	99.73 83

v144192		92.38 196	93.55 199	91.00 188	91.44 203	97.47 201	94.27 194	87.41 178	96.52 199	78.03 196	87.50 204	82.65 202	95.32 179	95.82 195	95.15 188	99.55 156	99.78 51

v1921920		92.36 198	93.57 197	90.94 189	91.39 205	97.39 204	94.70 185	87.63 177	96.60 197	76.63 201	86.98 208	82.89 199	95.75 164	96.26 185	95.14 189	99.55 156	99.73 83

test20.03		90.65 207	93.71 195	87.09 208	90.44 214	96.24 215	89.74 217	85.46 191	95.59 214	72.99 216	90.68 181	85.33 183	84.41 219	95.94 193	95.10 190	99.52 163	97.06 212

pmmvs5		92.71 189	94.27 182	90.90 190	91.42 204	97.74 181	93.23 199	86.66 184	95.99 209	78.96 194	91.45 175	83.44 194	95.55 171	97.30 155	95.05 191	99.58 147	98.93 181

v7n		91.61 203	92.95 204	90.04 197	90.56 213	97.69 184	93.74 198	85.59 190	95.89 211	76.95 199	86.60 210	78.60 222	93.76 202	97.01 164	94.99 192	99.65 117	99.87 18

v2v482		92.77 186	93.52 200	91.90 173	91.59 200	97.63 189	94.57 191	90.31 142	96.80 193	79.22 191	88.74 195	81.55 207	96.04 160	95.26 199	94.97 193	99.66 113	99.69 102

SCA		94.95 145	97.44 109	92.04 166	95.55 133	99.16 109	96.26 154	79.30 213	99.02 83	85.73 153	98.18 73	97.13 86	97.69 116	96.03 190	94.91 194	97.69 212	97.65 205

v8		92.87 181	93.87 194	91.72 177	92.05 184	97.50 199	94.79 182	88.20 170	96.85 191	80.11 187	90.01 185	82.86 200	95.48 174	95.15 202	94.90 195	99.66 113	99.80 37

V42		93.05 179	93.90 193	92.04 166	91.91 187	97.66 186	94.91 176	89.91 147	96.85 191	80.58 183	89.66 187	83.43 195	95.37 178	95.03 205	94.90 195	99.59 143	99.78 51

SixPastTwentyTwo		93.44 174	95.32 167	91.24 184	92.11 183	98.40 159	92.77 202	88.64 165	98.09 152	77.83 197	93.51 160	85.74 180	96.52 148	96.91 166	94.89 197	99.59 143	99.73 83

tpm		92.38 196	94.79 173	89.56 202	94.30 155	97.50 199	94.24 196	78.97 217	97.72 169	74.93 209	97.97 80	82.91 198	96.60 145	93.65 212	94.81 198	98.33 203	98.98 179

EPMVS		95.05 143	96.86 134	92.94 156	95.84 113	98.96 119	96.68 142	79.87 209	99.05 80	90.15 127	97.12 98	95.99 101	97.49 122	95.17 201	94.75 199	97.59 213	96.96 213

thisisatest0515		94.61 154	96.89 132	91.95 170	92.00 185	98.47 152	92.01 206	90.73 137	98.18 148	83.96 158	94.51 148	95.13 110	93.38 204	97.38 151	94.74 200	99.61 129	99.79 45

v148		92.36 198	92.88 205	91.75 175	91.63 198	97.66 186	92.64 203	90.55 140	96.09 205	83.34 166	88.19 198	80.00 214	92.74 208	93.98 211	94.58 201	99.58 147	99.69 102

TDRefinement		93.04 180	93.57 197	92.41 159	96.58 91	98.77 129	97.78 107	91.96 111	98.12 151	80.84 181	89.13 192	79.87 217	87.78 216	96.44 175	94.50 202	99.54 160	98.15 198

ADS-MVSNet		94.65 152	97.04 128	91.88 174	95.68 124	98.99 116	95.89 158	79.03 216	99.15 62	85.81 152	96.96 100	98.21 75	97.10 129	94.48 209	94.24 203	97.74 209	97.21 209

PatchmatchNet		94.70 150	97.08 126	91.92 171	95.53 134	98.85 124	95.77 160	79.54 211	98.95 88	85.98 150	98.52 59	96.45 90	97.39 125	95.32 198	94.09 204	97.32 215	97.38 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PM-MVS		89.55 210	90.30 215	88.67 205	87.06 220	95.60 218	90.88 209	84.51 198	96.14 204	75.75 203	86.89 209	63.47 231	94.64 189	96.85 168	93.89 205	99.17 191	99.29 161

pmmvs-eth3d		89.81 209	89.65 217	90.00 198	86.94 221	95.38 219	91.08 207	86.39 186	94.57 217	82.27 175	83.03 218	64.94 228	93.96 198	96.57 173	93.82 206	99.35 182	99.24 167

MDTV_nov1_ep13_2view		92.44 192	95.66 163	88.68 204	91.05 211	97.92 175	92.17 205	79.64 210	98.83 105	76.20 202	91.45 175	93.51 129	95.04 185	95.68 196	93.70 207	97.96 207	98.53 190

new_pmnet		90.45 208	92.84 207	87.66 207	88.96 217	96.16 216	88.71 219	84.66 196	97.56 172	71.91 219	85.60 213	86.58 174	93.28 205	96.07 189	93.54 208	98.46 200	94.39 221

N_pmnet		92.21 200	94.60 177	89.42 203	91.88 188	97.38 205	89.15 218	89.74 152	97.89 162	73.75 211	87.94 202	92.23 137	93.85 201	96.10 188	93.20 209	98.15 206	97.43 207

CostFormer		94.25 161	94.88 171	93.51 146	95.43 138	98.34 163	96.21 155	80.64 206	97.94 160	94.01 79	98.30 71	86.20 178	97.52 120	92.71 214	92.69 210	97.23 218	98.02 201

pmmvs3		88.19 213	91.27 212	84.60 215	85.60 223	93.66 223	85.68 223	81.13 204	92.36 223	63.66 228	89.51 188	77.10 224	93.22 206	96.37 178	92.40 211	98.30 204	97.46 206

tpmrst		93.86 169	95.88 160	91.50 178	95.69 122	98.62 142	95.64 163	79.41 212	98.80 110	83.76 163	95.63 138	96.13 98	97.25 126	92.92 213	92.31 212	97.27 216	96.74 214

MDA-MVSNet-bldmvs		87.84 214	89.22 218	86.23 211	81.74 225	96.77 213	83.74 224	89.57 154	94.50 218	72.83 217	96.64 110	64.47 230	92.71 209	81.43 225	92.28 213	96.81 220	98.47 192

Gipuma		81.40 218	81.78 221	80.96 219	83.21 224	85.61 230	79.73 227	76.25 225	97.33 178	64.21 227	55.32 227	55.55 232	86.04 217	92.43 217	92.20 214	96.32 223	93.99 222
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmnet_mix02		92.44 192	94.68 175	89.83 201	92.46 177	97.65 188	89.92 216	90.49 141	98.76 118	73.05 215	91.78 173	90.08 151	94.86 188	94.53 208	91.94 215	98.21 205	98.01 202

ambc			80.99 222		80.04 228	90.84 224	90.91 208		96.09 205	74.18 210	62.81 226	30.59 237	82.44 221	96.25 186	91.77 216	95.91 224	98.56 189

dps		94.63 153	95.31 168	93.84 135	95.53 134	98.71 137	96.54 146	80.12 208	97.81 168	97.21 28	96.98 99	92.37 134	96.34 152	92.46 216	91.77 216	97.26 217	97.08 211

tpm cat1		94.06 162	94.90 170	93.06 154	95.42 140	98.52 150	96.64 144	80.67 205	97.82 166	92.63 105	93.39 162	95.00 111	96.06 159	91.36 220	91.58 218	96.98 219	96.66 216

CMPMVS	70.31 18	90.74 205	91.06 213	90.36 196	97.32 76	97.43 202	92.97 201	87.82 176	93.50 220	75.34 208	83.27 217	84.90 187	92.19 211	92.64 215	91.21 219	96.50 222	94.46 220
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		81.36 219	89.93 216	71.35 222	88.65 218	87.85 228	71.46 230	88.12 172	96.23 201	32.21 234	92.61 171	83.00 197	56.27 229	91.92 219	89.43 220	91.39 228	88.49 225

new-patchmatchnet		86.12 216	87.30 219	84.74 214	86.92 222	95.19 222	83.57 225	84.42 199	92.67 222	65.66 223	80.32 220	64.72 229	89.41 214	92.33 218	89.21 221	98.43 201	96.69 215

PMMVS2		77.26 220	79.47 223	74.70 221	76.00 229	88.37 227	74.22 229	76.34 223	78.31 227	54.13 230	69.96 225	52.50 233	70.14 226	84.83 223	88.71 222	97.35 214	93.58 223

MVE	67.97 19	65.53 225	67.43 227	63.31 225	59.33 233	74.20 231	53.09 235	70.43 228	66.27 230	43.13 231	45.98 231	30.62 236	70.65 225	79.34 227	86.30 223	83.25 232	89.33 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				82.25 217	97.73 70	88.71 226	80.18 226	68.65 229	99.15 62	86.98 145	99.47 11	85.31 184	68.35 227	87.51 222	83.81 224	91.64 226

E-PMN		68.30 223	68.43 225	68.15 223	74.70 232	71.56 233	55.64 233	77.24 221	77.48 229	39.46 232	51.95 230	41.68 235	73.28 224	70.65 228	79.51 225	88.61 230	86.20 228

FPMVS		83.82 217	84.61 220	82.90 216	90.39 215	90.71 225	90.85 210	84.10 200	95.47 215	65.15 224	83.44 216	74.46 226	75.48 222	81.63 224	79.42 226	91.42 227	87.14 226

PMVS	72.60 17	76.39 221	77.66 224	74.92 220	81.04 227	69.37 234	68.47 231	80.54 207	85.39 226	65.07 225	73.52 224	72.91 227	65.67 228	80.35 226	76.81 227	88.71 229	85.25 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		68.12 224	68.11 226	68.14 224	75.51 231	71.76 232	55.38 234	77.20 222	77.78 228	37.79 233	53.59 228	43.61 234	74.72 223	67.05 229	76.70 228	88.27 231	86.24 227

testmvs		31.24 226	40.15 228	20.86 227	12.61 234	17.99 235	25.16 236	13.30 230	48.42 231	24.82 235	53.07 229	30.13 238	28.47 230	42.73 230	37.65 229	20.79 233	51.04 230

test123		26.75 227	34.25 229	18.01 228	7.93 235	17.18 236	24.85 237	12.36 231	44.83 232	16.52 236	41.80 232	18.10 239	28.29 231	33.08 231	34.79 230	18.10 234	49.95 231

uanet_test		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 228	0.00 230	0.00 229	0.00 237	0.00 237	0.00 238	0.00 233	0.00 233	0.00 238	0.00 233	0.00 240	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

TPM-MVS					99.57 26	98.90 122	98.79 58			96.52 37	98.62 57	99.91 31	97.56 119			99.44 172	99.28 162
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										69.05 221

9.14												99.79 45

SR-MVS					99.67 13			98.25 14				99.94 25

our_test_3					92.30 179	97.58 194	90.09 215

MTAPA										98.09 15		99.97 8

MTMP										98.46 10		99.96 12

Patchmatch-RL test							66.86 232

XVS					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 91

X-MVStestdata					97.42 74	99.62 33	98.59 65			93.81 85		99.95 17				99.69 91

mPP-MVS					99.53 30							99.89 35

NP-MVS									98.57 128

Patchmtry						98.59 145	97.15 131	79.14 214		80.42 184

DeepMVS_CX						96.85 211	87.43 221	89.27 156	98.30 143	75.55 206	95.05 142	79.47 218	92.62 210	89.48 221		95.18 225	95.96 218

High-resolution multi-view benchmark